| | --- |
| | license: mit |
| | tags: |
| | - brain-inspired |
| | - spiking-neural-network |
| | - multi-task-learning |
| | - continual-learning |
| | - modular-ai |
| | - biologically-plausible |
| | --- |
| | |
| | # ModularBrainAgent 🧠 |
| | **Author:** Aliyu Lawan Halliru (`@Almusawee`) |
| | **Affiliation:** Independent AI Researcher (Nigeria) |
| | **License:** MIT |
| | **Paper:** [Download PDF](./ModularBrainAgent_Paper.pdf) |
| | **Diagram:** (Coming soon) |
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| | --- |
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| | ## 🧠 Abstract |
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| | We propose ModularBrainAgent, a biologically motivated neural architecture for multi-task learning that mirrors the functional organization of the human brain. Unlike monolithic deep networks, our model is designed with architectural intelligence: distinct modular subsystems that reflect perceptual, attentional, memory, and decision-making pathways in biological cognition. |
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| | Each component — including spiking sensory processors, adaptive interneurons, relay routing layers, neuroendocrine gain modulators, recurrent autonomic loops, and mirror-state comparators — serves a unique cognitive function. These modules are not just trainable; they are structurally positioned to enable learning itself. This built-in cognitive topology improves sample efficiency, interpretability, and continual adaptability. |
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| | The model supports multimodal input via GRUs, CNNs, and shared encoders, and leverages a task-specific replay buffer for lifelong learning. Experimental design favors generalization across domains and tasks with minimal interference. We argue that structural cognition — not just data or gradient optimization — is the key to general-purpose artificial intelligence. ModularBrainAgent provides a functional and extensible blueprint for biologically plausible, task-flexible, and memory-capable AI systems. |
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| | --- |
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| | ## 📌 Architecture Overview |
| |
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| | - Spiking sensory neurons for input encoding |
| | - Attention-based relay for signal routing |
| | - Adaptive interneuron logic for abstraction |
| | - Neuroendocrine modulation (gain control) |
| | - GRU-based recurrent loop (autonomic memory) |
| | - Mirror comparator for goal-state reflection |
| | - Replay buffer with task tagging |
| | - Multimodal encoders and task heads |
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| | --- |
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| | ## 🤝 License |
| | MIT License (free to use, adapt, and build upon with attribution) |
| |
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| | ## 📝 Citation |
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| | > ⚠️ **Note**: This version of the model is a **working prototype**. |
| | > While the architecture is complete and documented, |
| | > training and module testing are ongoing. Contributions welcome. |
